`DImodels`

The `DImodels`

package is designed to make fitting
Diversity-Interactions models easier. Diversity-Interactions (DI) models
(Kirwan et al 2009) are a set of tools for analysing and interpreting
data from experiments that explore the effects of species diversity
(from a pool of *S* species) on community-level responses. Data
suitable for DI models will include (at least) for each experimental
unit: a response recorded at a point in time, and a set of proportions
of *S* species \(p_1\), \(p_2\), …, \(p_S\) from a point in time prior to the
recording of the response. The proportions sum to 1 for each
experimental unit.

**Main changes in the package from version 1.3 to version
1.3.1**

- A
`fortify`

function method has been added to supplement the data fitted to a linear model with model fit statistics. - A
`describe_model`

function is added which can be used to get a short text summary of any DI model. - Meta-data about a DI model can be accessed via the
`attributes`

function.

**Main changes in the package from version 1.2 to version
1.3**

- The
`DI`

and`autoDI`

functions now have an additional parameter called`ID`

which enables the user to group the species identity effects (see examples below). - The
`predict`

function now has flexibility to calculate confidence and prediction intervals for the predicted values.

**Main changes in the package from version 1.1 to version
1.2**

- There are two new functions added to the package:
`predict`

: Make predictions from a fitted DI model without having to worry about theta, and the interaction terms in the data.`contrasts_DI`

: Create contrasts for a DI model.

**Main changes in the package from version 1.0 to version
1.1**

`DI_data_prepare`

is now superseded by`DI_data`

(see examples below)

`DImodels`

installation and loadThe `DImodels`

package is installed from CRAN and loaded
in the typical way.

It is recommended that users unfamiliar with Diversity-Interactions
(DI) models read the introduction to `DImodels`

, before using
the package. Run the following code to access the documentation.

There are seven example datasets included in the
`DImodels`

package: `Bell`

, `sim1`

,
`sim2`

, `sim3`

, `sim4`

,
`sim5`

, `Switzerland`

. Details about each of these
datasets is available in their associated help files, run this code, for
example:

In this vignette, we will describe the `sim3`

dataset and
show a worked analysis of it.

The `sim3`

dataset was simulated from a functional group
(FG) Diversity-Interactions model. There were nine species in the pool,
and it was assumed that species 1 to 5 come from functional group 1,
species 6 and 7 from functional group 2 and species 8 and 9 from
functional group 3, where species in the same functional group are
assumed to have similar traits. The following equation was used to
simulate the data.

\[ y = \sum_{i=1}^{9}\beta_ip_i +
\omega_{11}\sum_{\substack{i,j = 1 \\ i<j}}^5p_ip_j +
\omega_{22}p_6p_7 + \omega_{33}p_8p_9 \\ + \omega_{12}\sum_{\substack{i
\in {1,2,3,4,5} \\ j \in {6,7}}}p_ip_j + \omega_{13}\sum_{\substack{i
\in {1,2,3,4,5} \\ j \in {8,9}}}p_ip_j + \omega_{23}\sum_{\substack{i
\in {6,7} \\ j \in {8,9}}}p_ip_j + \gamma_k + \epsilon\] Where
\(\gamma_k\) is a treatment effect with
two levels (*k = 1,2*) and \(\epsilon\) was assumed IID N(0, \(\sigma^2\)). The parameter values are in
the following table.

Parameter | Value | Parameter | Value | |
---|---|---|---|---|

\(\beta_1\) | 10 | \(\omega_{11}\) | 2 | |

\(\beta_2\) | 9 | \(\omega_{22}\) | 3 | |

\(\beta_3\) | 8 | \(\omega_{33}\) | 1 | |

\(\beta_4\) | 7 | \(\omega_{12}\) | 4 | |

\(\beta_5\) | 11 | \(\omega_{13}\) | 9 | |

\(\beta_6\) | 6 | \(\omega_{23}\) | 3 | |

\(\beta_7\) | 5 | \(\gamma_1\) | 3 | |

\(\beta_8\) | 8 | \(\gamma_2\) | 0 | |

\(\beta_9\) | 9 | \(\sigma\) | 1.2 |

Here, the non-linear parameter \(\theta\) that can be included as a power on each \(p_ip_j\) component of each interaction variable (Connolly et al 2013) was set equal to one and thus does not appear in the equation above.

The 206 rows of proportions contained in the dataset
`design_a`

(supplied in the package) were used to simulate
the `sim3`

dataset. Here is the first few rows from
`design_a`

:

community | richness | p1 | p2 | p3 | p4 | p5 | p6 | p7 | p8 | p9 |
---|---|---|---|---|---|---|---|---|---|---|

1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |

1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |

2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |

2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |

3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |

3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |

Where `community`

is an identifier for unique sets of
proportions and `richness`

is the number of species in the
community.

The proportions in `design_a`

were replicated over two
treatment levels, giving a total of 412 rows in the simulated dataset.
The `sim3`

data can be loaded and viewed in the usual
way.

community | richness | treatment | p1 | p2 | p3 | p4 | p5 | p6 | p7 | p8 | p9 | response |
---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 10.265 |

1 | 1 | B | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 7.740 |

1 | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 12.173 |

1 | 1 | B | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 8.497 |

2 | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 10.763 |

2 | 1 | B | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 8.989 |

2 | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 10.161 |

2 | 1 | B | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7.193 |

3 | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 10.171 |

3 | 1 | B | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6.053 |

There are several graphical displays that will help to explore the data and it may also be useful to generate summary statistics.

`autoDI`

The function `autoDI`

in `DImodels`

provides a
way to do an automated exploratory analysis to compare a range of DI
models. It works through a set of automated steps (Steps 1 to 4) and
will select the ‘best’ model from the range of models that have been
explored and test for lack of fit in that model. The selection process
is not exhaustive, but provides a useful starting point in analysis
using DI models.

```
auto1 <- autoDI(y = "response", prop = 4:12, treat = "treatment",
FG = c("FG1","FG1","FG1","FG1","FG1","FG2","FG2","FG3","FG3"), data = sim3,
selection = "Ftest")
#>
#> --------------------------------------------------------------------------------
#> Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures
#>
#> Theta estimate: 0.9714
#> Selection using F tests
#> Description
#> DI Model 1 Average interactions 'AV' DImodel with treatment
#> DI Model 2 Average interactions 'AV' DImodel with treatment, estimating theta
#>
#> DI_model treat estimate_theta Resid. Df Resid. SSq Resid. MSq
#> DI Model 1 AV 'treatment' FALSE 401 694.3095 1.7314
#> DI Model 2 AV 'treatment' TRUE 400 693.7321 1.7343
#> Df SSq F Pr(>F)
#> DI Model 1
#> DI Model 2 1 0.5775 0.333 0.5642
#>
#> The test concludes that theta is not significantly different from 1.
#>
#> --------------------------------------------------------------------------------
#> Step 2: Investigating the interactions
#> Since 'Ftest' was specified as selection criterion and functional groups were specified, dropping the ADD model as it is not nested within the FG model.
#> Selection using F tests
#> Description
#> DI Model 1 Structural 'STR' DImodel with treatment
#> DI Model 2 Species identity 'ID' DImodel with treatment
#> DI Model 3 Average interactions 'AV' DImodel with treatment
#> DI Model 4 Functional group effects 'FG' DImodel with treatment
#> DI Model 5 Separate pairwise interactions 'FULL' DImodel with treatment
#>
#> DI_model treat estimate_theta Resid. Df Resid. SSq Resid. MSq
#> DI Model 1 STR 'treatment' FALSE 410 1496.1645 3.6492
#> DI Model 2 ID 'treatment' FALSE 402 841.2740 2.0927
#> DI Model 3 AV 'treatment' FALSE 401 694.3095 1.7314
#> DI Model 4 FG 'treatment' FALSE 396 559.7110 1.4134
#> DI Model 5 FULL 'treatment' FALSE 366 522.9727 1.4289
#> Df SSq F Pr(>F)
#> DI Model 1
#> DI Model 2 8 654.8905 57.2903 <0.0001
#> DI Model 3 1 146.9645 102.8524 <0.0001
#> DI Model 4 5 134.5985 18.8396 <0.0001
#> DI Model 5 30 36.7383 0.857 0.686
#>
#> Selected model: Functional group effects 'FG' DImodel with treatment
#>
#> --------------------------------------------------------------------------------
#> Step 3: Investigating the treatment effect
#> Selection using F tests
#> Description
#> DI Model 1 Functional group effects 'FG' DImodel
#> DI Model 2 Functional group effects 'FG' DImodel with treatment
#>
#> DI_model treat estimate_theta Resid. Df Resid. SSq Resid. MSq
#> DI Model 1 FG none FALSE 397 1550.682 3.9060
#> DI Model 2 FG 'treatment' FALSE 396 559.711 1.4134
#> Df SSq F Pr(>F)
#> DI Model 1
#> DI Model 2 1 990.9711 701.12 <0.0001
#>
#> Selected model: Functional group effects 'FG' DImodel with treatment
#>
#> --------------------------------------------------------------------------------
#> Step 4: Comparing the final selected model with the reference (community) model
#> 'community' is a factor with 100 levels, one for each unique set of proportions.
#>
#> model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F)
#> DI Model 1 Selected 396 559.7110 1.4134
#> DI Model 2 Reference 311 445.9889 1.4340 85 113.7222 0.933 0.6423
#>
#> --------------------------------------------------------------------------------
#> autoDI is limited in terms of model selection. Exercise caution when choosing your final model.
#> --------------------------------------------------------------------------------
```

The output of `autoDI`

, works through the following
process:

- Step 1 fitted the average interactions (
`AV`

) model and uses profile likelihood to estimate the non-linear parameter \(\theta\) and tests whether or not it differs from one. \(\theta\) was estimated to be 0.96814 and was not significantly different from one (\(p = 0.4572\)). Therefore, subsequent steps assumed \(\theta=1\) when fitting the DI models. - Step 2 fitted five different DI models, each with a different form of species interactions and treatment was always included. The functional group model (FG) was the selected model. This assumes that pairs of species interact according to functional group membership.
- Step 3 provided a test for the treatment and indicated that the treatment, included as an additive factor, was significant and needed in the model (\(p < 0.0001\)).
- Step 4 provides a lack of fit test, here there was no indication of lack of fit in the model selected in Step 3 (\(p = 0.6423\)).

Further details on each of these steps are available in the
`autoDI`

help file. Run the following code to access the
documentation.

All parameter estimates from the selected model can be viewed using
`summary`

.

```
summary(auto1)
#>
#> Call:
#> glm(formula = new_fmla, family = family, data = new_data)
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> p1_ID 9.7497 0.3666 26.595 < 2e-16 ***
#> p2_ID 8.5380 0.3672 23.253 < 2e-16 ***
#> p3_ID 8.2329 0.3666 22.459 < 2e-16 ***
#> p4_ID 6.3644 0.3665 17.368 < 2e-16 ***
#> p5_ID 10.8468 0.3669 29.561 < 2e-16 ***
#> p6_ID 5.9621 0.4515 13.205 < 2e-16 ***
#> p7_ID 5.4252 0.4516 12.015 < 2e-16 ***
#> p8_ID 7.3204 0.4515 16.213 < 2e-16 ***
#> p9_ID 8.2154 0.4515 18.196 < 2e-16 ***
#> FG_bfg_FG1_FG2 3.4395 0.8635 3.983 8.09e-05 ***
#> FG_bfg_FG1_FG3 11.5915 0.8654 13.395 < 2e-16 ***
#> FG_bfg_FG2_FG3 2.8711 1.2627 2.274 0.02351 *
#> FG_wfg_FG1 2.8486 0.9131 3.120 0.00194 **
#> FG_wfg_FG2 0.6793 2.3553 0.288 0.77319
#> FG_wfg_FG3 2.4168 2.3286 1.038 0.29997
#> treatmentA 3.1018 0.1171 26.479 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 1.413412)
#>
#> Null deviance: 52280.33 on 412 degrees of freedom
#> Residual deviance: 559.71 on 396 degrees of freedom
#> AIC: 1329.4
#>
#> Number of Fisher Scoring iterations: 2
```

If the final model selected by autoDI includes a value of theta other
than 1, then a 95% confidence interval for \(\theta\) can be generated using the
`theta_CI`

function:

Here, this code would not run, since the final model selected by
`autoDI`

does not include theta estimated.

`DI`

functionFor some users, the selection process in `autoDI`

will be
sufficient, however, most users will fit additional models using
`DI`

. For example, while the treatment is included in
`autoDI`

as an additive factor, interactions between
treatment and other model terms are not considered. Here, we will first
fit the model selected by `autoDI`

using `DI`

and
then illustrate the capabilities of `DI`

to fit specialised
models.

`autoDI`

using
`DI`

```
m1 <- DI(y = "response", prop = 4:12,
FG = c("FG1","FG1","FG1","FG1","FG1","FG2","FG2","FG3","FG3"), treat = "treatment",
DImodel = "FG", data = sim3)
#> Warning in DI_data_prepare(y = y, block = block, density = density, prop = prop, : One or more rows have species proportions that sum to approximately 1, but not exactly 1. This is typically a rounding issue, and has been corrected internally prior to analysis.
#> Fitted model: Functional group effects 'FG' DImodel
summary(m1)
#>
#> Call:
#> glm(formula = new_fmla, family = family, data = new_data)
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> p1_ID 9.7497 0.3666 26.595 < 2e-16 ***
#> p2_ID 8.5380 0.3672 23.253 < 2e-16 ***
#> p3_ID 8.2329 0.3666 22.459 < 2e-16 ***
#> p4_ID 6.3644 0.3665 17.368 < 2e-16 ***
#> p5_ID 10.8468 0.3669 29.561 < 2e-16 ***
#> p6_ID 5.9621 0.4515 13.205 < 2e-16 ***
#> p7_ID 5.4252 0.4516 12.015 < 2e-16 ***
#> p8_ID 7.3204 0.4515 16.213 < 2e-16 ***
#> p9_ID 8.2154 0.4515 18.196 < 2e-16 ***
#> FG_bfg_FG1_FG2 3.4395 0.8635 3.983 8.09e-05 ***
#> FG_bfg_FG1_FG3 11.5915 0.8654 13.395 < 2e-16 ***
#> FG_bfg_FG2_FG3 2.8711 1.2627 2.274 0.02351 *
#> FG_wfg_FG1 2.8486 0.9131 3.120 0.00194 **
#> FG_wfg_FG2 0.6793 2.3553 0.288 0.77319
#> FG_wfg_FG3 2.4168 2.3286 1.038 0.29997
#> treatmentA 3.1018 0.1171 26.479 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 1.413412)
#>
#> Null deviance: 52280.33 on 412 degrees of freedom
#> Residual deviance: 559.71 on 396 degrees of freedom
#> AIC: 1329.4
#>
#> Number of Fisher Scoring iterations: 2
```

`autoDI`

estimating theta using `update_DI`

```
m1_theta <- update_DI(object = m1, estimate_theta = TRUE)
#> Warning in DI_data_prepare(y = y, block = block, density = density, prop = prop, : One or more rows have species proportions that sum to approximately 1, but not exactly 1. This is typically a rounding issue, and has been corrected internally prior to analysis.
#> Fitted model: Functional group effects 'FG' DImodel
#> Theta estimate: 0.9681
coef(m1_theta)
#> p1_ID p2_ID p3_ID p4_ID p5_ID
#> 9.8128865 8.6069092 8.2968619 6.4287580 10.9110563
#> p6_ID p7_ID p8_ID p9_ID FG_bfg_FG1_FG2
#> 6.0189395 5.4846833 7.4038925 8.2992262 2.9840924
#> FG_bfg_FG1_FG3 FG_bfg_FG2_FG3 FG_wfg_FG1 FG_wfg_FG2 FG_wfg_FG3
#> 10.6019235 2.3514998 2.3737831 0.3789464 1.8470612
#> treatmentA theta
#> 3.1017864 0.9681005
```

The species identity effects in a DI model can be grouped by
specifying groups for each species using the `ID`

argument.
The `ID`

argument functions similar to the `FG`

argument and accepts a character list of same length as number of
species in the model. The identity effects of species belonging in the
same group will be grouped together.

Grouping all identity effects into a single term

```
m1_group <- update_DI(object = m1_theta,
ID = c("ID1", "ID1", "ID1", "ID1", "ID1",
"ID1", "ID1", "ID1", "ID1"))
#> Warning in DI_data_prepare(y = y, block = block, density = density, prop = prop, : One or more rows have species proportions that sum to approximately 1, but not exactly 1. This is typically a rounding issue, and has been corrected internally prior to analysis.
#> Fitted model: Functional group effects 'FG' DImodel
#> Theta estimate: 0.9919
coef(m1_group)
#> ID1 FG_bfg_FG1_FG2 FG_bfg_FG1_FG3 FG_bfg_FG2_FG3 FG_wfg_FG1
#> 7.8667702 1.1475018 12.9438529 -1.2235215 5.6141823
#> FG_wfg_FG2 FG_wfg_FG3 treatmentA theta
#> -5.5214662 1.0205019 3.1017864 0.9919097
```

Grouping identity effects of specific species

```
m1_group2 <- update_DI(object = m1_theta,
ID = c("ID1", "ID1", "ID1",
"ID2", "ID2", "ID2",
"ID3", "ID3", "ID3"))
#> Warning in DI_data_prepare(y = y, block = block, density = density, prop = prop, : One or more rows have species proportions that sum to approximately 1, but not exactly 1. This is typically a rounding issue, and has been corrected internally prior to analysis.
#> Fitted model: Functional group effects 'FG' DImodel
#> Theta estimate: 0.989
coef(m1_group2)
#> ID1 ID2 ID3 FG_bfg_FG1_FG2 FG_bfg_FG1_FG3
#> 8.5288216 7.9537767 7.1357104 0.9665077 13.3434768
#> FG_bfg_FG2_FG3 FG_wfg_FG1 FG_wfg_FG2 FG_wfg_FG3 treatmentA
#> 0.4940952 4.1543637 -4.4683501 3.4674196 3.1017864
#> theta
#> 0.9889999
```

Note: Grouping ID effects will not have an effect on the calculation of the interaction effects, they would still be calculated by using all species.

Read the documentation of `DI`

and `autoDI`

for
more information and examples using the `ID`

parameter.

`DI`

functionThere are two ways to fit customised models using `DI`

;
the first is by using the option `DImodel =`

in the
`DI`

function and adding the argument
`extra_formula =`

to it, and the second is to use the
`custom_formula`

argument in the `DI`

function. If
species interaction variables (e.g., the FG interactions or the average
pairwise interaction) are included in either `extra_formula`

or `custom_formula`

, they must first be created and included
in the dataset. The function `DI_data`

can be used to compute
several types of species interaction variables.

`extra_formula`

```
m2 <- DI(y = "response", prop = 4:12,
FG = c("FG1","FG1","FG1","FG1","FG1","FG2","FG2","FG3","FG3"), treat = "treatment",
DImodel = "FG", extra_formula = ~ (p1 + p2 + p3 + p4):treatment,
data = sim3)
#> Warning in DI_data_prepare(y = y, block = block, density = density, prop = prop, : One or more rows have species proportions that sum to approximately 1, but not exactly 1. This is typically a rounding issue, and has been corrected internally prior to analysis.
#> Fitted model: Functional group effects 'FG' DImodel
summary(m2)
#>
#> Call:
#> glm(formula = new_fmla, family = family, data = new_data)
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> p1_ID 10.018491 0.466552 21.473 < 2e-16 ***
#> p2_ID 8.494038 0.467009 18.188 < 2e-16 ***
#> p3_ID 7.970716 0.466536 17.085 < 2e-16 ***
#> p4_ID 6.624476 0.466443 14.202 < 2e-16 ***
#> p5_ID 10.802270 0.378776 28.519 < 2e-16 ***
#> p6_ID 5.917565 0.461482 12.823 < 2e-16 ***
#> p7_ID 5.380703 0.461535 11.658 < 2e-16 ***
#> p8_ID 7.275881 0.461506 15.766 < 2e-16 ***
#> p9_ID 8.170907 0.461471 17.706 < 2e-16 ***
#> FG_bfg_FG1_FG2 3.439508 0.865279 3.975 8.38e-05 ***
#> FG_bfg_FG1_FG3 11.591458 0.867140 13.367 < 2e-16 ***
#> FG_bfg_FG2_FG3 2.871063 1.265295 2.269 0.02381 *
#> FG_wfg_FG1 2.848612 0.915008 3.113 0.00199 **
#> FG_wfg_FG2 0.679285 2.360195 0.288 0.77365
#> FG_wfg_FG3 2.416774 2.333420 1.036 0.30097
#> treatmentA 3.190868 0.216493 14.739 < 2e-16 ***
#> `treatmentA:p1` -0.626667 0.668369 -0.938 0.34902
#> `treatmentA:p2` -0.001213 0.668369 -0.002 0.99855
#> `treatmentA:p3` 0.435322 0.668369 0.651 0.51522
#> `treatmentA:p4` -0.609180 0.668369 -0.911 0.36262
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 1.419257)
#>
#> Null deviance: 52280.33 on 412 degrees of freedom
#> Residual deviance: 556.35 on 392 degrees of freedom
#> AIC: 1335
#>
#> Number of Fisher Scoring iterations: 2
```

`extra_formula`

First, we create the FG pairwise interactions, using the
`DI_data`

function with the `what`

argument set to
`"FG"`

.

```
FG_matrix <- DI_data(prop = 4:12, FG = c("FG1","FG1","FG1","FG1","FG1","FG2","FG2","FG3","FG3"),
data = sim3, what = "FG")
sim3a <- data.frame(sim3, FG_matrix)
```

Then we fit the model using `extra_formula`

.

```
m3 <- DI(y = "response", prop = 4:12,
FG = c("FG1","FG1","FG1","FG1","FG1","FG2","FG2","FG3","FG3"),
treat = "treatment", DImodel = "FG",
extra_formula = ~ (bfg_FG1_FG2 + bfg_FG1_FG3 + bfg_FG2_FG3 +
wfg_FG1 + wfg_FG2 + wfg_FG3) : treatment, data = sim3a)
#> Warning in DI_data_prepare(y = y, block = block, density = density, prop = prop, : One or more rows have species proportions that sum to approximately 1, but not exactly 1. This is typically a rounding issue, and has been corrected internally prior to analysis.
#> Fitted model: Functional group effects 'FG' DImodel
summary(m3)
#>
#> Call:
#> glm(formula = new_fmla, family = family, data = new_data)
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> p1_ID 9.68668 0.40000 24.217 < 2e-16 ***
#> p2_ID 8.47495 0.40053 21.159 < 2e-16 ***
#> p3_ID 8.16990 0.39998 20.426 < 2e-16 ***
#> p4_ID 6.30140 0.39987 15.759 < 2e-16 ***
#> p5_ID 10.78379 0.40031 26.938 < 2e-16 ***
#> p6_ID 5.89908 0.47958 12.301 < 2e-16 ***
#> p7_ID 5.36222 0.47963 11.180 < 2e-16 ***
#> p8_ID 7.25740 0.47960 15.132 < 2e-16 ***
#> p9_ID 8.15243 0.47957 17.000 < 2e-16 ***
#> FG_bfg_FG1_FG2 4.00191 1.12383 3.561 0.000415 ***
#> FG_bfg_FG1_FG3 11.77389 1.12973 10.422 < 2e-16 ***
#> FG_bfg_FG2_FG3 3.83681 1.64287 2.335 0.020027 *
#> FG_wfg_FG1 2.81860 1.16226 2.425 0.015757 *
#> FG_wfg_FG2 -1.58378 3.11717 -0.508 0.611682
#> FG_wfg_FG3 1.32358 3.07561 0.430 0.667181
#> treatmentA 3.22783 0.33480 9.641 < 2e-16 ***
#> `treatmentA:bfg_FG1_FG2` -1.12480 1.43053 -0.786 0.432178
#> `treatmentA:bfg_FG1_FG3` -0.36487 1.44450 -0.253 0.800717
#> `treatmentA:bfg_FG2_FG3` -1.93150 2.09024 -0.924 0.356029
#> `treatmentA:wfg_FG1` 0.06003 1.42911 0.042 0.966517
#> `treatmentA:wfg_FG2` 4.52613 4.06260 1.114 0.265924
#> `treatmentA:wfg_FG3` 2.18638 3.99748 0.547 0.584733
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 1.42436)
#>
#> Null deviance: 52280.3 on 412 degrees of freedom
#> Residual deviance: 555.5 on 390 degrees of freedom
#> AIC: 1338.3
#>
#> Number of Fisher Scoring iterations: 2
```

`custom_formula`

First, we create a dummy variable for level A of the treatment (this
is required for the `glm`

engine that is used within
`DI`

and because there is no intercept in the model).

Then we fit the model using `custom_formula`

.

```
m3 <- DI(y = "response",
custom_formula = response ~ 0 + p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9 +
treatmentA + bfg_FG1_FG2 + bfg_FG1_FG3 + bfg_FG2_FG3, data = sim3a)
#> Fitted model: Custom DI model
summary(m3)
#>
#> Call:
#> glm(formula = custom_formula, family = family, data = data)
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> p1 10.3417 0.3138 32.957 < 2e-16 ***
#> p2 9.1766 0.3103 29.573 < 2e-16 ***
#> p3 8.8268 0.3134 28.164 < 2e-16 ***
#> p4 6.9742 0.3122 22.341 < 2e-16 ***
#> p5 11.4422 0.3141 36.426 < 2e-16 ***
#> p6 5.9177 0.3994 14.815 < 2e-16 ***
#> p7 5.3967 0.3999 13.496 < 2e-16 ***
#> p8 7.4468 0.3983 18.695 < 2e-16 ***
#> p9 8.3449 0.3984 20.945 < 2e-16 ***
#> treatmentA 3.1018 0.1184 26.198 < 2e-16 ***
#> bfg_FG1_FG2 2.9359 0.8042 3.651 0.000296 ***
#> bfg_FG1_FG3 10.8896 0.8343 13.053 < 2e-16 ***
#> bfg_FG2_FG3 2.9410 1.2233 2.404 0.016667 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 1.443887)
#>
#> Null deviance: 52280.33 on 412 degrees of freedom
#> Residual deviance: 576.11 on 399 degrees of freedom
#> AIC: 1335.3
#>
#> Number of Fisher Scoring iterations: 2
```

We can make predictions from a DI model just like any other
regression model using the `predict`

function. The user does
not need to worry about adding any interaction terms or adjusting any
columns if theta is not equal to 1. Only the species proportions along
with any additional experimental structures is needed and all other
terms in the model will be calculated for the user.

```
# Fit model
m3 <- DI(y = "response", prop = 4:12,
treat = "treatment", DImodel = "AV",
extra_formula = ~ (AV) : treatment, data = sim3a)
#> Warning in DI_data_prepare(y = y, block = block, density = density, prop = prop, : One or more rows have species proportions that sum to approximately 1, but not exactly 1. This is typically a rounding issue, and has been corrected internally prior to analysis.
#> Fitted model: Average interactions 'AV' DImodel
predict_data <- sim3[c(1, 79, 352), 3:12]
# Only species proportions and treatment is needed
print(predict_data)
#> treatment p1 p2 p3 p4 p5 p6 p7 p8
#> 1 A 0 0 0.0000000 0 0.0000000 0.0000000 0.0000000 0.0000000
#> 79 A 0 0 0.0000000 0 0.5000000 0.0000000 0.0000000 0.5000000
#> 352 B 0 0 0.1666667 0 0.1666667 0.1666667 0.1666667 0.1666667
#> p9
#> 1 1.0000000
#> 79 0.0000000
#> 352 0.1666667
# Make prediction
predict(m3, newdata = predict_data)
#> 1 79 352
#> 12.83789 14.27503 10.00291
```

```
# The interval and level parameters can be used to calculate the
# uncertainty around the predictions
# Get confidence interval around prediction
predict(m3, newdata = predict_data, interval = "confidence")
#> fit lwr upr
#> 1 12.83789 12.028716 13.64707
#> 79 14.27503 13.817612 14.73246
#> 352 10.00291 9.694552 10.31126
# Get prediction interval around prediction
predict(m3, newdata = predict_data, interval = "prediction")
#> fit lwr upr
#> 1 12.83789 10.124779 15.55100
#> 79 14.27503 11.645310 16.90476
#> 352 10.00291 7.394976 12.61083
# The function returns a 95% interval by default,
# this can be changed using the level argument
predict(m3, newdata = predict_data,
interval = "prediction", level = 0.9)
#> fit lwr upr
#> 1 12.83789 10.562595 15.11319
#> 79 14.27503 12.069670 16.48040
#> 352 10.00291 7.815819 12.18999
```

The `contrasts_DI`

function can be used to compare and
formally test for a difference in performance of communities within the
same as well as across different experimental structures

Comparing the performance of the monocultures of different species at treatment A

```
contr <- list("p1vsp2" = c(1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
"p3vsp5" = c(0, 0, 1, 0, -1, 0, 0, 0, 0, 0, 0, 0),
"p4vsp6" = c(0, 0, 0, 1, 0, -1, 0, 0, 0, 0, 0, 0),
"p7vsp9" = c(0, 0, 0, 0, 0, 0, 1, 0, -1, 0, 0, 0))
the_C <- contrasts_DI(m3, contrast = contr)
#> Generated contrast matrix:
#> p1_ID p2_ID p3_ID p4_ID p5_ID p6_ID p7_ID p8_ID p9_ID AV treatmentA
#> p1vsp2 1 -1 0 0 0 0 0 0 0 0 0
#> p3vsp5 0 0 1 0 -1 0 0 0 0 0 0
#> p4vsp6 0 0 0 1 0 -1 0 0 0 0 0
#> p7vsp9 0 0 0 0 0 0 1 0 -1 0 0
#> `AV:treatmentB`
#> p1vsp2 0
#> p3vsp5 0
#> p4vsp6 0
#> p7vsp9 0
summary(the_C)
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Fit: glm(formula = new_fmla, family = family, data = new_data)
#>
#> Linear Hypotheses:
#> Estimate Std. Error z value Pr(>|z|)
#> p1vsp2 == 0 1.473 0.477 3.088 0.00803 **
#> p3vsp5 == 0 -2.652 0.477 -5.560 1.08e-07 ***
#> p4vsp6 == 0 1.462 0.477 3.064 0.00870 **
#> p7vsp9 == 0 -5.521 0.477 -11.573 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> (Adjusted p values reported -- single-step method)
```

Comparing across the two treatment levels for monoculture of species 1

```
contr <- list("treatAvsB" = c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0))
the_C <- contrasts_DI(m3, contrast = contr)
#> Generated contrast matrix:
#> p1_ID p2_ID p3_ID p4_ID p5_ID p6_ID p7_ID p8_ID p9_ID AV treatmentA
#> treatAvsB 1 0 0 0 0 0 0 0 0 0 1
#> `AV:treatmentB`
#> treatAvsB 0
summary(the_C)
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Fit: glm(formula = new_fmla, family = family, data = new_data)
#>
#> Linear Hypotheses:
#> Estimate Std. Error z value Pr(>|z|)
#> treatAvsB == 0 12.8993 0.4116 31.34 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> (Adjusted p values reported -- single-step method)
```

Comparing between two species mixtures

```
mixA <- c(0.25, 0, 0.25, 0, 0.25, 0, 0.25, 0, 0, 0, 0, 0)
mixB <- c(0, 0.3333, 0, 0.3333, 0, 0.3333, 0, 0, 0, 0, 0, 0)
# We have the proportions of the individual species in the mixtures, however
# we still need to calculate the interaction effect for these communities
contr_data <- data.frame(rbind(mixA, mixB))
colnames(contr_data) <- names(coef(m3))
# Adding the interaction effect of the two mixtures
contr_data$AV <- DI_data_E_AV(prop = 1:9, data = contr_data)$AV
print(contr_data)
#> p1_ID p2_ID p3_ID p4_ID p5_ID p6_ID p7_ID p8_ID p9_ID AV
#> mixA 0.25 0.0000 0.25 0.0000 0.25 0.0000 0.25 0 0 0.3750000
#> mixB 0.00 0.3333 0.00 0.3333 0.00 0.3333 0.00 0 0 0.3332667
#> treatmentA `AV:treatmentB`
#> mixA 0 0
#> mixB 0 0
# We can now subtract the respective values in each column of the two
# mixtures and get our contrast
my_contrast <- as.matrix(contr_data[1, ] - contr_data[2, ])
rownames(my_contrast) <- "mixAvsB"
the_C <- contrasts_DI(m3, contrast = my_contrast)
#> Generated contrast matrix:
#> p1_ID p2_ID p3_ID p4_ID p5_ID p6_ID p7_ID p8_ID p9_ID AV
#> mixAvsB 0.25 -0.3333 0.25 -0.3333 0.25 -0.3333 0.25 0 0 0.04173333
#> treatmentA `AV:treatmentB`
#> mixAvsB 0 0
summary(the_C)
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Fit: glm(formula = new_fmla, family = family, data = new_data)
#>
#> Linear Hypotheses:
#> Estimate Std. Error z value Pr(>|z|)
#> mixAvsB == 0 2.0379 0.2599 7.841 4.44e-15 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> (Adjusted p values reported -- single-step method)
```

Connolly J, T Bell, T Bolger, C Brophy, T Carnus, JA Finn, L Kirwan, F Isbell, J Levine, A Lüscher, V Picasso, C Roscher, MT Sebastia, M Suter and A Weigelt (2013) An improved model to predict the effects of changing biodiversity levels on ecosystem function. Journal of Ecology, 101, 344-355.

Kirwan L, J Connolly, JA Finn, C Brophy, A Lüscher, D Nyfeler and MT Sebastia (2009) Diversity-interaction modelling - estimating contributions of species identities and interactions to ecosystem function. Ecology, 90, 2032-2038.